Last edited time
Apr 15, 2025 2:37 PM
/type
AIKnowledge
/tech-category
/pitch
A comparison of leading large language models and their features.
/read-time
5 min
/tldr
- The document compares various large language models (LLMs) from different companies, detailing their release dates, parameters, capabilities, and strengths/weaknesses. - Notable models include OpenAI's GPT-4, Google's Gemini 2.0, and Meta's LLaMA 4, each with distinct features and performance metrics. - The landscape of LLMs showcases a mix of closed and open ecosystems, emphasizing advancements in multimodal capabilities and context length.
/target-persona
1. Data Scientist 2. AI Researcher 3. Product Manager
The Battle of Large Language Models (LLMs)
Company Model | Release | Params | OS | Multimodal | Context Length | MMLU | HumanEval | Strength | Weakness |
OpenAI – GPT-4 | Mar 2024 | Not disclosed (~1T est.) | Text, Image input | 8K–128K | 86.4% | 67% | Strong reasoning, fluent writing, top-tier API ecosystem | Closed, slow, expensive, dated cutoff | |
Google – Gemini 2.0 | Dec 2024 | 200B–1.5T (unconfirmed) | Text, Image I/O, Audio | Up to 1M (claims) | 90% | ~74% | Multimodal native, agentic, integrates with Google ecosystem | Closed, limited access to Ultra, uneven polish | |
xAI – Grok 3 | Feb 2025 | Not disclosed (massive compute) | Partially | Text, Image input | > GPT-4 (claimed) | > GPT-4 (claimed) | Unfiltered, real-time X data, good STEM | Unpolished, edgy, limited reach | |
Meta – LLaMA 4 | Apr 2025 | 109B, 400B | Yes | Text, Image input | 128K | ~86% | 85% | Open weights, huge community, long context | Hardware heavy, tuning needed for safety |
DeepSeek – R1 | Jan 2025 | 670B (MoE) | Yes | Text only | 85% est. | 85% est. | Efficient, open, GPT-4 parity at 1/10 cost | New, less polished | |
Anthropic – Claude 3 | Mar 2024 | Not disclosed (~70B+ est.) | Text, Image input | 100K–200K | 86.8% | 84.9% | Long context, safe, fast, cheaper than GPT-4 | Verbose, closed | |
Cohere – Command A | Mar 2025 | 111B | Partially | Text | 256K | N/A | N/A | Fast, efficient, enterprise-ready | Not creative, closed ecosystem |
Amazon – Nova Pro | Dec 2024 | Undisclosed (~100B+ est.) | Text, Image, Video planned | 32K | >= GPT-4 (claims) | >= GPT-4 (claims) | Tight AWS integration, enterprise features | Opaque, no public access | |
Mistral – Mixtral, Pixtral | 2024 | 7B, 8x7B MoE, 124B | Yes | Pixtral: Vision | 131K | ~70% (7B), >85% (124B) | ~85% (124B) | Open, fast, compact, state-of-the-art vision | Small 7B needs tuning, large models restricted license |
Alibaba – Qwen 2.5 | Jan 2025 | 7B–72B open, Max MoE | Partially | Text, Image, Audio, Code | 32K | 85%+ | 85%+ | Chinese NLP leader, specialized variants | Moderated, English feedback limited |